AI Integrity Window Optimization Using Knowledge Graph Asset Correlation
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Solution Overview
Problem
Traditional data analytics and digital transformation for industrial assets are inefficient due to the need for human interaction and difficulty in determining inter-relationships between data from multiple systems, leading to time-consuming and resource-intensive processes.
Innovation Solution
A system utilizing a knowledge graph data structure to correlate operational technology data and provide insights, allowing for automated adjustment of operational limits and proactive issue identification through a cognitive advisor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analytics methods are used with human interaction, then insights can be obtained from asset data, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system enables automated self-service analytics where the cognitive advisor independently processes asset data, correlates information from multiple systems, and generates insights without requiring human analyst intervention. This automation resolves the contradiction by eliminating manual analysis time while maintaining insight quality through AI-driven correlation algorithms.
Solution Approach 2:
The patent replaces the mechanical human analysis process with an electronic cognitive advisor system that uses machine learning and knowledge graphs to automatically correlate asset data. This substitution eliminates the time loss associated with manual analysis while preserving measurement precision through sophisticated digital correlation methods.
2Reliability
If a specialized worker monitors a large number of assets, then comprehensive oversight is achieved, but issue identification becomes difficult and inefficient
Solution Approach 1:
The system segments the monitoring task by assigning specific asset portfolios to individual cognitive advisors, each specialized in analyzing data from particular asset types or systems. This segmentation maintains comprehensive oversight reliability while improving issue identification efficiency by distributing the analytical workload across multiple specialized AI agents rather than overloading a single human worker.
Solution Approach 2:
The cognitive advisor system provides universal monitoring capabilities that can handle diverse asset types and data sources simultaneously. Each cognitive advisor is designed to correlate data across multiple systems and asset categories, achieving comprehensive oversight reliability while maintaining high productivity through automated multi-functional analysis that exceeds human capacity.
3Loss of information
If data from multiple systems is analyzed to determine inter-relationships, then comprehensive insights are obtained, but the process becomes complex and time-consuming
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary data structure that standardizes and correlates information from multiple disparate systems. The knowledge graph serves as a mediator that transforms complex multi-system data into structured relationships, achieving complete data correlation while reducing perceived system complexity by providing a unified view through graphical representation and automated correlation algorithms.
Solution Approach 2:
The system adds a new dimension to data analysis by implementing temporal correlation capabilities that examine how asset relationships evolve over time. This dimensional addition enables comprehensive insights into dynamic inter-relationships between systems while managing complexity through time-based pattern recognition and predictive analytics that simplify the interpretation of multi-system interactions.
4Use of energy by moving object
If limited time is spent on data modeling, then resource consumption is reduced, but insight quality deteriorates
Solution Approach 1:
The system performs preliminary action by pre-building and maintaining knowledge graphs that capture relationships between assets, systems, and data sources before analysis is needed. This preliminary structuring of data enables rapid querying and correlation during actual analysis, reducing real-time computing resource consumption while maintaining high insight quality through pre-processed relational structures.
Solution Approach 2:
The cognitive advisor dynamically adjusts analysis parameters such as correlation depth, data sampling rates, and modeling complexity based on the specific asset portfolio and query requirements. This parameter optimization enables the system to consume fewer computing resources by applying appropriate levels of analytical depth, while maintaining insight quality by increasing parameters only when necessary for the specific analysis task.
Data Source
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AI summary
Various embodiments described herein relate to an artificial intelligence system for integrity operating window optimization related to one or more assets. In this regard, a request to obtain one or more insights related to one or more assets is received. The request includes an asset descriptor describing the one or more assets. In response to the request, aspects of aggregated operational technology data within a knowledge graph data structure are correlated to provide the one or more insights. Additionally, one or more operational limits for the one or more assets are adjusted based on the one or more insights associated with the knowledge graph data structure.